TensorFlow: Powerful Predictive Analytics with TensorFlow

Book description

Learn how to solve real life problems using different methods like logic regression, random forests and SVM's with TensorFlow.

About This Book

  • Understand predictive analytics along with its challenges and best practices
  • Embedded with assessments that will help you revise the concepts you have learned in this book

Who This Book Is For

This book is aimed at developers, data analysts, machine learning practitioners, and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow.

What You Will Learn

  • Learn TensorFlow features in a real-life problem, followed by detailed TensorFlow installation and configuration
  • Explore computation graphs, data, and programming models also get an insight into an example of implementing linear regression model for predictive analytics
  • Solve the Titanic survival problem using logistic regression, random forests, and SVMs for predictive analytics
  • Dig deeper into predictive analytics and find out how to take advantage of it to cluster records belonging to the certain group or class for a dataset of unsupervised observations
  • Learn several examples of how to apply reinforcement learning algorithms for developing predictive models on real-life datasets

In Detail

Predictive analytics discovers hidden patterns from structured and unstructured data for automated decision making in business intelligence. Predictive decisions are becoming a huge trend worldwide, catering to wide industry sectors by predicting which decisions are more likely to give maximum results. TensorFlow, Google's brainchild, is immensely popular and extensively used for predictive analysis.

This book is a quick learning guide on all the three types of machine learning, that is, supervised, unsupervised, and reinforcement learning with TensorFlow. This book will teach you predictive analytics for high-dimensional and sequence data. In particular, you will learn the linear regression model for regression analysis. You will also learn how to use regression for predicting continuous values. You will learn supervised learning algorithms for predictive analytics. You will explore unsupervised learning and clustering using K-meansYou will then learn how to predict neighborhoods using K-means, and then, see another example of clustering audio clips based on their audio features.

This book is ideal for developers, data analysts, machine learning practitioners, and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow.

This book is embedded with useful assessments that will help you revise the concepts you have learned in this book.

Style and approach

This is a fast-paced guide that provides a quick learning solution to all the three types of machine learning, that is, supervised, unsupervised, and reinforcement learning with TensorFlow

Note: This book is a blend of text and quizzes, all packaged up keeping your journey in mind. It includes content from the following Packt product:

  • Predictive Analytics with TensorFlow by Md. Rezaul Karim

Publisher resources

Download Example Code

Table of contents

  1. TensorFlow: Powerful Predictive Analytics with TensorFlow
    1. TensorFlow: Powerful Predictive Analytics with TensorFlow
    2. Credits
      1. Meet Your Expert
    3. Preface
      1. What's in It for Me?
      2. What Will I Get From This Book?
      3. Prerequisites
    4. 1. From Data to Decisions – Getting Started with TensorFlow
      1. Taking Decisions Based on Data – Titanic Example
        1. Data Value Chain for Making Decisions
        2. From Disaster to Decision – Titanic Survival Example
      2. General Overview of TensorFlow
      3. Installing and Configuring TensorFlow
        1. Installing TensorFlow on Linux
          1. Installing Python and nVidia Driver
          2. Installing NVIDIA CUDA
          3. Installing NVIDIA cuDNN v5.1+
          4. Installing the libcupti-dev Library
          5. Installing TensorFlow
            1. Installing TensorFlow with native pip
            2. Installing with virtualenv
        2. Installing TensorFlow from Source
        3. Testing Your TensorFlow Installation
      4. TensorFlow Computational Graph
      5. TensorFlow Programming Model
      6. Data Model in TensorFlow
        1. Tensors
        2. Rank
        3. Shape
        4. Data Type
        5. Variables
        6. Fetches
        7. Feeds and Placeholders
      7. TensorBoard
        1. How Does TensorBoard Work?
      8. Getting Started with TensorFlow – Linear Regression and Beyond
        1. Source Code for the Linear Regression
      9. Summary
      10. Assessments
    5. 2. Putting Data in Place – Supervised Learning for Predictive Analytics
      1. Supervised Learning for Predictive Analytics
      2. Linear Regression – Revisited
        1. Problem Statement
        2. Using Linear Regression for Movie Rating Prediction
      3. From Disaster to Decision – Titanic Example Revisited
        1. An Exploratory Analysis of the Titanic Dataset
        2. Feature Engineering
        3. Logistic Regression for Survival Prediction
          1. Using TensorFlow Contrib
        4. Linear SVM for Survival Prediction
        5. Ensemble Method for Survival Prediction – Random Forest
        6. A Comparative Analysis
      4. Summary
      5. Assessments
    6. 3. Clustering Your Data – Unsupervised Learning for Predictive Analytics
      1. Unsupervised Learning and Clustering
      2. Using K-means for Predictive Analytics
        1. How K-means Works
        2. Using K-means for Predicting Neighborhoods
      3. Predictive Models for Clustering Audio Files
      4. Using kNN for Predictive Analytics
        1. Working Principles of kNN
        2. Implementing a kNN-Based Predictive Model
      5. Summary
      6. Assessments
    7. 4. Using Reinforcement Learning for Predictive Analytics
      1. Reinforcement Learning
      2. Reinforcement Learning in Predictive Analytics
      3. Notation, Policy, and Utility in RL
        1. Policy
        2. Utility
      4. Developing a Multiarmed Bandit's Predictive Model
      5. Developing a Stock Price Predictive Model
      6. Summary
      7. Assessments
    8. A. Assessment Answers
      1. Lesson 1: From Data to Decisions – Getting Started with TensorFlow
      2. Lesson 2: Putting Data in Place – Supervised Learning for Predictive Analytics
      3. Lesson 3: Clustering Your Data – Unsupervised Learning for Predictive Analytics
      4. Lesson 4: Using Reinforcement Learning for Predictive Analytics

Product information

  • Title: TensorFlow: Powerful Predictive Analytics with TensorFlow
  • Author(s): Md. Rezaul Karim
  • Release date: March 2018
  • Publisher(s): Packt Publishing
  • ISBN: 9781789136913